In this work we address the problem of short-term load forecasting. We propose a generalization of the linear state-space model where the evolution of the state and the observation matrices is unknown. The proposed blind Kalman filter algorithm proceeds via alternating the estimation of these unknown matrices and the inference of the state, within the framework of expectation-maximization. A mini-batch processing strategy is introduced to allow on-the-fly forecasting. The experimental results show that the proposed method outperforms the state-of-the-art techniques by a considerable margin, both on load profile estimation and peak load forecast problems.

中文翻译：

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用于短期负荷预测的盲卡尔曼滤波


在这项工作中，我们解决了短期负荷预测的问题。我们提出了线性状态空间模型的推广，其中状态和观测矩阵的演化是未知的。所提出的盲卡尔曼滤波器算法通过在期望最大化的框架内交替估计这些未知矩阵和状态推断来进行。引入小批量处理策略以允许即时预测。实验结果表明，所提出的方法在负载分布估计和峰值负载预测问题上都远远优于最先进的技术。